Our last highlight featured Parti founder, Bryan, pushing boundaries by giving a voice to the dead. Come check out the latest highlight. It can be difficult for us to imagine the future of AI, as it is something that does not yet exist. However, we are starting to get glimpses of what it might look like. AI already has the power to entice anyone, and it is becoming clear that it will play a major role in our future - even in ways like this.

An image generator is a software that creates images from the prompt text that can be used many purposes such in Graphic designing, Book templates etc. Many companies will use Image generator for creating new designs for anything they want. Many people will also use image generators for more traditional purposes, such as creating memes and creating artworks. Image generators are very useful because they can create an unlimited number of images without the need to find models or real world images. Even sometimes images are purely fictions but it does look like it fictions. DALL-E 2: OpenAI DALL-E 2 is an AI model developed by OpenAI that has been trained to generate images from text .

A green sign that says "Very Deep Learning" and is at the edge of the Grand Canyon. Puffy white clouds are in the sky. There is a river in front of them with water lilies.

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the "latte-drinking liberal." We develop a machine learning classifier to infer political leaning from non-political text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classifier on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We find no relationship between the level of political activity and our classification results. We apply our classifier to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left-right divide while sports news does not.

We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.